The South American monsoon approaches a critical transition in response to deforestation

The Amazon rainforest is threatened by land-use change and increasing drought and fire frequency. Studies suggest an abrupt dieback of large parts of the rainforest after partial forest loss, but the critical threshold, underlying mechanisms, and possible impacts of forest degradation on the monsoon circulation remain uncertain. Here, we use a nonlinear dynamical model of the moisture transport and recycling across the Amazon to identify several precursor signals for a critical transition in the coupled atmosphere-vegetation dynamics. Guided by our simulations, we reveal both statistical and physical precursor signals of an approaching critical transition in reanalysis and observational data. In accordance with our model results, we attribute these characteristic precursor signals to the nearing of a critical transition of the coupled Amazon atmosphere-vegetation system induced by forest loss due to deforestation, droughts, and fires. The transition would lead to substantially drier conditions, under which the rainforest could likely not be maintained.


Figure S2 :
Figure S2: Histogram of Kendall τ derived from 100,000 surrogate time series of lag-one autocorrelation and variance of the modelled precipitation rates.(a) Histogram of lag-one autocorrelation from surrogate time series generated by phase randomisation (see Methods).The dashed orange line denotes the threshold corresponding to a p-value of p = 0.1, and the black line denotes Kendall τ derived from the original modelled time series series (see Fig. 4 a).(b) Same as (a) but for variance.

Figure S3 :
Figure S3: Wet season onset and retreat date in southern Amazonia.(a) Wet season onset date in southern Amazonia (5 − 15 • S, 50 • − 70 • W) from 1979-2019 for the three different methods used (see methods).Dashed lines denote the corresponding linear trends.We find an increase of 3.5 (blue), 5.6 (orange) and 1.4 pentad/century (green) for the three methods, respectively.(b) Same as (a)but for the wet season retreat date.We find a decrease of −0.9 (blue), −1.0 (orange), and −1.6 pentad/century (green) of the retreat date.Data is taken from the ERA5 reanalysis (58 ).The wet season hence tends to initiate later and end earlier, consistently with an increase in DSL.

Figure S4 :
Figure S4: Map of trends in dry season length for the southern Amazon in ERA5.Calculated linear trend in the dry season length from 1979-2019 in southern Amazonia, based on the ERA5 reanalysis, using the three different methods used (see methods).Areas with p-value < 0.05 are hatched.(a) Dry season length calculated with method 1, (b) Calculated with method 2, (c) calculated with Method 3.All three methods show the same spatial pattern.

Figure S5 :
Figure S5: Regions with simultaneous increase in variance and autocorrelation in ERA5.The red areas denote regions where variance and autocorrelation in ERA5 precipitation rates increase.See Fig. 6 and method section for methods.

Figure S7 :
Figure S7: Temporal lag-one autocorrelation in different precipitation data sets.Spatial patterns of Kendall τ for the lag-one autocorrelation of observed monthly averaged precipitation time series for different precipitation data sets.The autocorrelation is calculated for rolling windows of width w = 20 years.The trend is determined by Kendall τ of the respective indicator of the detrended and deseasoned precipitation time series at every site.Stippling marks regions with significantly increasing trends (p < 0.05, see Methods for details on the statistical test.Mean wet season wind fields (1979-2019) at 750 hPa are delineated in white.The non-linear trend of the underlying precipitation time series is removed via STL (85 ) with a trend smoother length of 5 years and seasonal smoother length of 13 months.(a) For ERA5 precipitation (1979-2019).This subpanel is identical with Fig. 6a.(b) CHIRPS (1981-2022) (79 ) (c) GPCC (1982-2020) (76 ) (d) GPCP (1979-2022) (80 ).

Figure S8 :Figure S9 :Figure S10 :
Figure S8: Temporal variance in different precipitation data sets.Spatial patterns of Kendall τ for the variance of observed monthly averaged precipitation time series for different precipitation data sets.The variance is calculated for rolling windows of width w = 20 years.The trend is determined by Kendall τ of the respective indicator of the detrended and de-seasoned precipitation time series at every site.Stippling marks regions with significantly increasing trends (p < 0.05, see Methods for details on the statistical test.Mean wet season wind fields (1979-2019) at 750 hPa are delineated in white.The non-linear trend of the underlying precipitation time series is removed via STL (85 ) with a trend smoother length of 5 years and seasonal smoother length of 13 months.(a) For ERA5 precipitation (1979-2019).This subpanel is identical with Fig. 6b.(b) CHIRPS (1981-2022) (79 ) (c) GPCC (1982-2020) (76 ) (d) GPCP (1979-2022) (80 ).

Figure S11 :
Figure S11: Simulated wet season precipitation for different soil moisture deficit thresholds and reductions of the evapotranspiration after successive deforestation.(a) Wet season precipitation P DJF in box 90 for different values of the soil moisture deficit threshold SMD after successive deforestation.The atmospheric heating over the Atlantic ocean ⟨H⟩ AO = (120 ± 20) W/m 2 and the amplification factor AF = 2.5 are fixed.Deforestation is initiated at year 10 and is completed at year 30.All simulations show dieback of the rainforest prior to 50% deforestation.(b) Same as (a) but for different values of the reduction of the evapotranspiration after deforestation ∆E.